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Artificial Intelligence (AI) in M&A

Master cutting-edge AI implementation strategies and practical skills for modern deal-making

A view of the Earth from space at night showing the lights of cities and towns

A half-day course on AI tools for M&A presented in a virtual class from 9:00 am to 1:15 pm UK time

pdf Download:   Course Outline

Session 1 - Introduction and Overview

This section of the course is designed to equip M&A professionals with a clear, concise overview of artificial intelligence (AI) and machine learning (ML). Participants will learn key definitions and terminology that demystify what AI systems are, what they can do, and where their limitations lie. 

To achieve this, we provide a practical understanding of core machine learning concepts, as well as deep neural networks and large language models. The course then covers common failure modes of AI systems, mitigation strategies, and ethical and environmental considerations. This will assist participants to work more effectively with modern AI tools, knowing which tasks they are well-suited for, and how to avoid common pitfalls.

  • Overview of AI & Machine Learning
    • Understanding the landscape
    • Key Definitions
    • Tasks/Use Cases
  • Machine Learning Basics
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • Deep Neural Networks
    • Basics
    • Historical Context
    • Uses & Benefits
  • Large Language Models
    • Models
    • Training Pipeline
    • Evaluation & Fine Tuning
  • Current AI Limitations
    • Failure Modes
    • User Mitigation Strategies
  • Ethical & Environmental Considerations
    • Data Security
    • Energy Consumption
    • Automation
  • Myth-busting

Session 2: Core Prompting Principles for M&A

Foundation Principles

  • Context Setting
    • Deal background (transaction type, size, industry)
    • Stage of the process (initial review, due diligence, negotiation)
    • Specific requirements or constraints
  • Output Structuring
    • Format specification (report, analysis, term sheet)
    • The level of detail required
    • Key metrics or focus areas
  • Task Definition
    • Clear objectives
    • Specific deliverables
    • Timeline considerations
  • Exercise on Due Diligence Prompt Structure: Reviewing SPA for a 500M software company acquisition

Session 3: M&A-Specific Prompting Examples

  • Valuation Analysis Prompts [15 mins]
    • Trading Comparables Analysis: Analyse the following peer group for a B2B SaaS company
    • DCF Analysis Review: Review DCF model assumptions
  • Negotiation Support Prompts [15 mins]
    • Deal Positioning: Develop negotiation strategy for Initial price positioning
    • Risk Assessment: Analyse transaction risks for Legal/regulatory issues
  • Documentation Prompts for AI M&A Deals [15 mins]
    • SPA Review: Analyse key SPA sections, e.g. Purchase price adjustment mechanism
    • Term Sheet Development: Draft key terms
  • AI tools for financial Due Diligence: Prompts [15 mins]
    • Contract Analysis: Review customer contracts
    • Financial DD: Analyse the quality of the earnings report
  • Templates for Common M&A Tasks [15 mins]
    • Document Analysis Template: review key terms, risk areas
    • Financial Analysis Template: Analyse key financial metrics

Session 4: Practical application through case study

This session consolidates the programme's learning through an immersive case study based on the acquisition of TechServe, a €750M B2B SaaS company. You will apply your newly acquired AI prompting skills to real-world M&A scenarios, working through legal and financial analysis challenges using simplified versions of authentic transaction documents. The session provides hands-on experience crafting and refining prompts for LLMs (e.g. ChatGPT or Claude), focusing on high-impact areas such as the SPA, Qoe analysis, and earn-out mechanics.

Part 1: SPA Equity Bridge & Accounting Policies [20 mins]

  • Document: SPA Extract – Equity Bridge & Accounting Policies
  • Participants will use LLMs to analyse key sections of a draft SPA, focusing on:
    • Working capital target and adjustment mechanics
    • Relevance and clarity of accounting policies
    • Potential for dispute or ambiguity
    • Group Discussion:
      • Review outputs and discuss how different prompt styles affect interpretation and issue spotting.
Part 2: Quality of Earnings (QoE) Red Flags [20 mins]
  • Document: Extract from QoE report
  • Participants will prompt the LLM to identify material issues in revenue recognition, EBITDA adjustments, and working capital analysis.
    •  Discussion:
      • What risks did the AI pick up or miss? How can prompts be improved to ensure robust analysis?
Part 3: Earn-out Mechanism Analysis [20 mins]
  • Document: Standalone Earn-out Clause
  • Using LLMs, participants will evaluate an earn-out structure focusing on:
    • Definition of financial metrics (e.g. EBITDA)
    • Payment timing and calculation
    • Dispute resolution processes
    • Risk of manipulation or misinterpretation
    •  Discussion:
      • Participants compare prompts and outputs, flagging vague drafting or key negotiation points.
Part 4: Term Sheet Legal & Commercial Review [20 mins]
  • Document: Draft Term Sheet
  • Participants will analyse the term sheet using LLMs, focusing on:
    • Pricing structure
    • Conditions precedent
    • Exclusivity and anti-leakage clauses
    • High-level legal and commercial risk analysis
    •  Discussion:
      • Live debrief comparing LLM insights, prompt iterations, and human judgment.

Course Logistics

This is an interactive, hands-on AI in mergers and acquisitions course delivered via Zoom on public dates. In-house courses can be arranged upon request which can be virtual or in-person.

To ensure a smooth experience and get the most out of the session, please prepare the following in advance:

Tech Requirements:

  • Please use a laptop or desktop (not a mobile device).
  • Ensure your Zoom is up to date and functioning with audio/video.
  • You will need access to a large language model (LLM) — either:
    • ChatGPT Plus (recommended)
    • Claude Pro (acceptable alternative)

We will ask all participants to use the same platform for consistency, and we’ll confirm which one in advance.

LLM Setup:

  • Ensure you have a working subscription and can log in without issues.
  • You’ll receive basic onboarding instructions to help you structure and test your prompts before the session

Pre-Course Materials

You will be sent a small pack of simplified case study documents in Word format a few days before the session. These documents will form the basis for interactive exercises, including:

  • An extract from a Sale and Purchase Agreement (SPA)
  • A sample Quality of Earnings (QoE) report
  • A standalone Earn-out clause
  • A draft Term Sheet

Please review these documents in advance, and have them open and accessible during the session (either in Word or PDF format).

Course Content Updates

This programme has been designed to reflect current best practices in M&A and AI integration, but the landscape is evolving rapidly.

We may update or adjust the content in light of:

  • Developments in AI capabilities (e.g. major LLM releases)
  • Legal or regulatory guidance
  • Market Feedback and Case Law Trends

 You will receive the latest version of the materials shortly before the session.

 Participation Style

 This is not a lecture – it’s an interactive workshop. You’ll be asked to:

  • Work in breakout groups on short AI-driven exercises
  • Share your insights and prompt strategies
  • Compare and critique outputs from AI tools
  • Ask questions, offer feedback, and apply your judgment

The session is designed to be practical, reflective, and collaborative — so come ready to experiment and challenge ideas.

Session 1 of this AI in M&A course is led by a trainer pursuing a PhD in Machine Learning at the Gatsby Unit of University College London (UCL). His research primarily revolves around developing novel machine learning algorithms with greater efficiency and robustness guarantees. His work has been previously published and selected for presentation at the International Conference on Artificial Intelligence and Statistics (AISTATS). The Gatsby Unit has produced ground-breaking AI research since its inception in 1998. Its alumni include two 2024 Nobel Prize winners in Physics and Chemistry and the co-founders of DeepMind - one of the world's leading AI research labs.

This trainer also has over 5 years of commercial experience applying advanced AI techniques during his previous role as an econometrician/data scientist at PwC. His previous projects included:
  • Using machine learning to estimate the growth impact of SME lending for a leading retail bank
  • Developing PwC’s AI Nowcasting Model for accurate UK GDP predictions
  • Causal machine learning to estimate the effectiveness of marketing activities for a global airline
  • Econometric methods to analyse the effect of CEO contract incentives on share buybacks for the UK Department of Business, Energy and Industrial Strategy.
Academically, he has achieved distinctions in two master's degrees from UCL - an MSc in Computational Statistics and Machine Learning in 2021 (Dean’s List) and an MSc in Economics in 2016.

Sessions 2 – 4 are led by a consultant, public speaker, and author with over 45 years of experience in private equity, debt advisory, restructuring, and infrastructure. He is a Senior Advisor to KPMG Finland and a Senior Consultant to Grant Thornton UK.

The consultant provides training programmes to a wide range of blue-chip clients in Europe, Africa, the Middle and Far East, North America, Asia-Pacific and China. In-house clients include banks (BNP Paribas, Société Générale, ING, Barclays Capital, Bank of China, RBS, SEB); lawyers (Kirkland and Ellis, Baker & McKenzie, Skadden Arps, Sullivan & Cromwell, Cadwalader, Latham & Watkins, Weil, White & Case); advisory firms (Lazard, PWC, M&A International, KPMG, EY USA, Deloitte); PE firms (Cinven, Advent, Barings Asia, Waterland, AVCAL); corporates (Siemens, Airbus, Turkcell, Candy Crush, Diageo, Statkraft) and governmental bodies (the UKLA, the EBRD, the EIB, the ECGD, Omani Oil Corp.)

He qualified in South Africa both as a Chartered Accountant - with Deloitte and as a lawyer with Hofmeyr - where he was involved in structuring several high-profile project financings including BMW 3 Series, Ford Sierra and GM.

After moving to London, he built an extensive career in corporate finance, serving as a corporate finance executive at Lazard Brothers, an assistant director at Hoare Govett advising listed companies and later joining ABN Amro's cross-border M&A team before becoming a Director in Cross-Border M&A at MeesPierson Corporate Finance. Separately, he has served as a member of the EU-PHARE programme and advised the Estonian government on its privatisation programme.

For 18 years he served as the Programme Director at the City Business School, London, for Infrastructure Finance for the M.Sc. programme in Business Administration and Finance. He has since stepped back from this role to focus on select advisory and consulting engagements.

He also served for approximately 10 years as an advisor to DebtXplain (subsequently acquired by Reorg and now Octus), bringing his extensive knowledge in debt markets and financial restructuring to the organisation before recently transitioning away from this role.

He is a fellow of the Institute of Chartered Accountants in England & Wales and the South African Institute of Chartered Accountants.

Upon completion of this AI in Mergers and Acquisitions course, participants will be able to:
  • Develop sophisticated prompt engineering techniques tailored for M&A applications, ensuring consistent and reliable AI outputs.
  • Evaluate and select the best AI tools for M&A workflows, understanding their capabilities, limitations, and optimal use cases.
  • Create robust quality control frameworks for AI-generated outputs in high-stakes transaction environments.
  • Implement effective risk management protocols of AI for M&A due diligence, contract review, and financial analysis.
  • Structure and execute artificial intelligence for M&A due diligence that maintains accuracy while significantly improving efficiency.
  • Develop strategies for managing AI limitations and biases in M&A contexts, ensuring reliable and trustworthy outputs.
  • Create comprehensive documentation protocols for AI usage in M&A processes, meeting regulatory and compliance requirements.

  • Investment Banking Professionals:
    • M&A associates and directors seeking to enhance deal execution efficiency
    • Financial modelling specialists looking to integrate AI tools
    • Due diligence teams aiming to automate routine analysis
    • Deal sourcing professionals interested in AI-powered screening tools
  • Private Equity and Venture Capital Professionals:
    • Deal teams seeking to streamline transaction processes
    • Portfolio operations managers implementing AI solutions
    • Investment analysts focusing on tech-enabled deal evaluation
    • Due diligence specialists looking to enhance their toolkit
  • Legal Professionals:
    • M&A lawyers wanting to leverage AI for contract review
    • Corporate lawyers handling transaction documentation
    • Legal technology officers implementing AI solutions
  • Corporate Development Executives:
    • M&A strategy leaders at corporations
    • Corporate development teams that manage deal pipelines
    • Integration specialists handling post-merger processes
  • Financial Advisory Professionals:
    • Transaction advisory teams at professional services firms
    • Valuation specialists incorporating AI modelling
    • Due diligence professionals seeking efficiency gains
    • Deal consultants advising on modern M&A practices
  • Risk and Compliance Professionals:
    • Deal compliance officers managing AI implementation
    • Risk management specialists in M&A contexts

This comprehensive program equips M&A professionals with cutting-edge AI implementation strategies and practical skills for modern deal-making.

Through a carefully structured blend of theoretical foundations and hands-on applications, you will learn to effectively integrate AI tools for M&A across the entire lifecycle - from deal sourcing and due diligence to post-merger integration.

Redcliffe's AI in mergers and acquisitions workshop combines a technical understanding of AI capabilities with practical M&A applications, featuring real-world case studies and interactive workshops. Gain proficiency in prompt engineering specifically tailored for M&A contexts, master quality control processes for AI outputs, and develop strategies for managing AI limitations in high-stakes transaction environments.
Number of places:

£ 895.00

Discounts available:

  • 2 places at 20% less
  • 3 places at 30% less
  • 4+ places at 40% less
  • Select the number of course places and dates to automatically calculate the discount
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